Recognizing Human Faces from Videos

We present an exemplar-based probabilistic approach for face recognition in video data. The approach has two stages: First, Exemplars, which are selected representatives from the raw video, are automatically extracted from gallery videos. The exemplars are used to summarize the gallery video information. In the second part, these exemplars are then used as centers for probabilistic mixture distributions for the tracking and recognition process. Here, we exploit the fact, that tracking and recognition are related problems. The use of probabilistic methods are attractive because they allow a systematic handling of uncertainty and an elegant way for fusing temporal information. We tested our approach on more than 100 training and testing sequences, with 25 different individuals.